Indoor positioning system based on BLE location fingerprinting with classification approach

Position estimation is an important technique for location-based services. Many services and applications, such as navigation assistance, surveillance of patients and social networking, have been developed based on users’ position. Although the GPS plays an important role in positioning systems, its signal strength is extremely weak inside buildings. Thus, other sensing devices are necessary to improve the accuracy of indoor localisation. In the past decade, researchers have developed a series of indoor positioning technologies based on the received signal strength (RSS) of WiFi, ZigBee or Bluetooth devices under the infrastructure of wireless sensor network for location estimation. We can compute the distance of the devices by measuring their RSS, but the correctness of the result is unsatisfactory because the radio signal interference is a considerable issue and the indoor radio propagation is too complicated to model. Using the location fingerprint to estimate a target position is a feasible strategy because the location fingerprint records the characteristics of the signals and the signal strength is related to the space relation. This type of algorithm estimates the location of a target by matching online measurements with the closest a-priori location fingerprints. The matching or classification algorithm is a key issue in the correctness of location fingerprinting. In this paper, we propose an effective location fingerprinting algorithm based on the general and weighted k-nearest neighbour algorithms to estimate the position of the target node. The grid points are trained with an interval of 2 m, and the estimated position error is about 1.8 m. Thus, the proposed method is low computation consumption, and with an acceptable accuracy.     

超尺寸物品装箱问题

本文给出一类新的装箱问题，超尺寸物品装箱问题。就实际解决该问题所普遍彩的两步法，证明了当采用经典目标函数并且拆分次数不超过2时，第二步采用FFDLR的渐进最坏比为3/2。进而针对超尺寸物品装箱问题的算法提出了一个评价效率更高的目标函数。证明了在此目标函数下，当不限制物品的最大尺寸时，第二步采用最优装法两步法的渐近最坏比为2。最后，给出渐近最坏与拆分次数的关系。     

求解阵列天线自适应滤波问题的一种调比随机逼近算法

提出了求解阵列天线自适应滤波问题的一种调比随机逼近算法.每一步迭代中,算法选取调比的带噪负梯度方向作为新的迭代方向.相比已有的其他随机逼近算法,这个算法不需要调整稳定性常数,在一定程度上解决了稳定性常数选取难的问题.数值仿真实验表明,算法优于已有的滤波算法,且比经典Robbins-Monro （RM）算法具有更好的稳定性.     

Static and Dynamic Layout Problems with Varying Areas

This paper describes a two-step algorithm for solving the layout problem while assuming the departments can have varying areas. The first step solves a quadratic assignment problem formulation of the problem using a heuristic cutting plane routine. The second step solves a mixed-integer linear programming prob- lem to find the desired block diagram layout. The algorithm incorporates two concepts to make the solu- tions more practical. First, rearrangement costs are simultaneously considered along with flow costs in solving a dynamic layout problem involving multiple time periods. It is the only algorithm to solve a general dynamic layout problem with varying department areas. Second, regular department shapes are maintained by requiring all departments to be rectangular. Its formulation for doing this is more efficient than previous algorithms.     

基于灰色关联聚类的协同过滤推荐算法

针对协同过滤推荐系统具有数据的高稀疏,高维度,数据量大的特点,本文将灰色关联聚类与协同过虑推荐算法相结合,构建了灰色关联聚类的协同过滤推荐算法,将其应用到协同过滤推荐系统中,以解决数据具有高稀疏高维度的特性情况下的个性化推荐质量问题。首先,定义了推荐系统中的用户项目评分矩阵,用户灰色绝对关联度,用户灰色相似度,用户灰色关联聚类。然后,给出了灰色关联聚类的协同过滤推荐算法的计算方法和步骤,同时给出了评价推荐质量方法。最后,将本文算法与基于余弦,相关分析及修正的余弦等协同过滤推荐算法在大小不同的数据集下进行了实验,实验表明灰色关联聚类的协同过滤推荐算法相较于传统的协同过滤推荐方法具有推荐质量高,计算量小,对数据大小要求不高等优点,同时在推荐系统的冷启动,稳定性和计算效率方面也具有一定的优势。     

Functional data clustering: a survey

Clustering techniques for functional data are reviewed. Four groups of clustering algorithms for functional data are proposed. The first group consists of methods working directly on the evaluation points of the curves. The second groups is defined by filtering methods which first approximate the curves into a finite basis of functions and second perform clustering using the basis expansion coefficients. The third groups is composed of methods which perform simultaneously dimensionality reduction of the curves and clustering, leading to functional representation of data depending on clusters. The last group consists of distance-based methods using clustering algorithms based on specific distances for functional data. A software review as well as an illustration of the application of these algorithms on real data are presented.     

聚类分析中非0,1编码的遗传算法

K-平均算法属于聚类分析中的动态聚类法,但其聚类效果受初始聚类分类或初始点的影响较大。本文提出一种遗传算法(GA)来进行近代初始分类,以内部聚类准则作为评价指标,实验结果表明,该算法明显好于K-平均算法。     

A new approach to pre-processing digital image for wavelet-based watermark

The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman–Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.     

A Bayesian algorithm for global optimization

A crucial step in global optimization algorithms based on random sampling in the search domain is decision about the achievement of a prescribed accuracy. In order to overcome the difficulties related to such a decision, the Bayesian Nonparametric Approach has been introduced. The aim of this paper is to show the effectiveness of the approach when an ad hoc clustering technique is used for obtaining promising starting points for a local search algorithm. Several test problems are considered.     

A novel fuzzy C-means algorithm to generate diverse and desirable cluster solutions used by genetic-based clustering ensemble algorithms

One of the most significant discussions in the field of machine learning today is on the clustering ensemble. The clustering ensemble combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known for their high ability to solve optimization problems, especially the problem of the clustering ensemble. To date, despite the major contributions to find consensus cluster partitions with application of genetic algorithms, there has been little discussion on population initialization through generative mechanisms in genetic-based clustering ensemble algorithms as well as the production of cluster partitions with favorable fitness values in first phase clustering ensembles. In this paper, a threshold fuzzy C-means algorithm, named TFCM, is proposed to solve the problem of diversity of clustering, one of the most common problems in clustering ensembles. Moreover, TFCM is able to increase the fitness of cluster partitions, such that it improves performance of genetic-based clustering ensemble algorithms. The fitness average of cluster partitions generated by TFCM are evaluated by three different objective functions and compared against other clustering algorithms. In this paper, a simple genetic-based clustering ensemble algorithm, named SGCE, is proposed, in which cluster partitions generated by the TFCM and other clustering algorithms are used as the initial population used by the SGCE. The performance of the SGCE is evaluated and compared based on the different initial populations used. The experimental results based on eleven real world datasets demonstrate that TFCM improves the fitness of cluster partitions and that the performance of the SGCE is enhanced using initial populations generated by the TFCM.     

Genetic algorithm-tuned entropy-based fuzzy C-means algorithm for obtaining distinct and compact clusters

A modified approach had been developed in this study by combining two well-known algorithms of clustering, namely fuzzy c-means algorithm and entropy-based algorithm. Fuzzy c-means algorithm is one of the most popular algorithms for fuzzy clustering. It could yield compact clusters but might not be able to generate distinct clusters. On the other hand, entropy-based algorithm could obtain distinct clusters, which might not be compact. However, the clusters need to be both distinct as well as compact. The present paper proposes a modified approach of clustering by combining the above two algorithms. A genetic algorithm was utilized for tuning of all three clustering algorithms separately. The proposed approach was found to yield both distinct as well as compact clusters on two data sets.     

An Asynchronous Distributed Expectation Maximization Algorithm for Massive Data: The DEM Algorithm

The family of expectation--maximization (EM) algorithms provides a general approach to fitting flexible models for large and complex data. The expectation (E) step of EM-type algorithms is time-consuming in massive data applications because it requires multiple passes through the full data. We address this problem by proposing an asynchronous and distributed generalization of the EM called the distributed EM (DEM). Using DEM, existing EM-type algorithms are easily extended to massive data settings by exploiting the divide-and-conquer technique and widely available computing power, such as grid computing. The DEM algorithm reserves two groups of computing processes called workers and managers for performing the E step and the maximization step (M step), respectively. The samples are randomly partitioned into a large number of disjoint subsets and are stored on the worker processes. The E step of DEM algorithm is performed in parallel on all the workers, and every worker communicates its results to the managers at the end of local E step. The managers perform the M step after they have received results from a γ-fraction of the workers, where γ is a fixed constant in (0, 1]. The sequence of parameter estimates generated by the DEM algorithm retains the attractive properties of EM: convergence of the sequence of parameter estimates to a local mode and linear global rate of convergence. Across diverse simulations focused on linear mixed-effects models, the DEM algorithm is significantly faster than competing EM-type algorithms while having a similar accuracy. The DEM algorithm maintains its superior empirical performance on a movie ratings database consisting of 10 million ratings. Supplementary material for this article is available online.     

Accelerating large partial EVD/SVD calculations by filtered block Davidson methods

Partial eigenvalue decomposition (PEVD) and partial singular value decomposition (PSVD) of large sparse matrices are of fundamental importance in a wide range of applications, including latent semantic indexing, spectral clustering, and kernel methods for machine learning. The more challenging problems are when a large number of eigenpairs or singular triplets need to be computed. We develop practical and efficient algorithms for these challenging problems. Our algorithms are based on a filter-accelerated block Davidson method. Two types of filters are utilized, one is Chebyshev polynomial filtering, the other is rational-function filtering by solving linear equations. The former utilizes the fastest growth of the Chebyshev polynomial among same degree polynomials; the latter employs the traditional idea of shift-invert, for which we address the important issue of automatic choice of shifts and propose a practical method for solving the shifted linear equations inside the block Davidson method. Our two filters can efficiently generate high-quality basis vectors to augment the projection subspace at each Davidson iteration step, which allows a restart scheme using an active projection subspace of small dimension. This makes our algorithms memory-economical, thus practical for large PEVD/PSVD calculations. We compare our algorithms with representative methods, including ARPACK, PROPACK, the randomized SVD method, and the limited memory SVD method. Extensive numerical tests on representative datasets demonstrate that, in general, our methods have similar or faster convergence speed in terms of CPU time, while requiring much lower memory comparing with other methods. The much lower memory requirement makes our methods more practical for large-scale PEVD/PSVD computations.     

Fuzzy clustering using multiple Gaussian kernels with optimized-parameters

In this paper, we propose a new kernel-based fuzzy clustering algorithm which tries to find the best clustering results using optimal parameters of each kernel in each cluster. It is known that data with nonlinear relationships can be separated using one of the kernel-based fuzzy clustering methods. Two common fuzzy clustering approaches are: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn’t work well with “multiple-density” clusters, multiple kernel-based fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the best) parameters. Our algorithm is an extension of the single kernel-based fuzzy c-means and the multiple kernel-based fuzzy clustering algorithms. In this algorithm, there is no need to give “good” parameters of each kernel and no need to give an initial “good” number of kernels. Every cluster will be characterized by a Gaussian kernel with optimal parameters. In order to show its effective clustering performance, we have compared it to other similar clustering algorithms using different databases and different clustering validity measures.     

Hit-and-run algorithms for the identification of nonredundant linear inequalities

Two probabilistic hit-and-run algorithms are presented to detect nonredundant constraints in a full dimensional system of linear inequalities. The algorithms proceed by generating a random sequence of interior points whose limiting distribution is uniform, and by searching for a nonredundant constraint in the direction of a random vector from each point in the sequence. In the hypersphere directions algorithm the direction vector is drawn from a uniform distribution on a hypersphere. In the computationally superior coordinate directions algorithm a search is carried out along one of the coordinate vectors. The algorithms are terminated through the use of a Bayesian stopping rule. Computational experience with the algorithms and the stopping rule will be reported.     

基于随机采样模糊聚类和卡尔曼滤波方法的模糊辨识

提出一种新的基于模糊聚类和卡尔曼滤波方法的模糊辨识算法 .该方法是基于快速模糊聚类 ,计算给定样本在各类中的隶属度 ,并利用卡尔曼滤波方法辨识模糊模型的结论参数 .整个辨识过程与一般的模糊聚类方法 [1 ]相比 ,需要的 CPU时间大大缩短 .最后通过仿真实例验证了该方法的有效性 .     

一类一维在线单位聚类问题的随机近似算法

在给定的度量空间中, 单位聚类问题就是寻找最少的单位球来覆盖给定的所有点。这是一个众所周知的组合优化问题, 其在线版本为: 给定一个度量空间, 其中的n个点会一个接一个的到达任何可能的位置, 在点到达的时候必须给该点分配一个单位聚类, 而此时未来点的相关信息都是未知的, 问题的目标是最后使用的单位聚类数目最少。本文考虑的是带如下假设的一类一维在线单位聚类问题: 在相应离线问题的最优解中任意两个相邻聚类之间的距离都大于0.5。本文首先给出了两个在线算法和一些引理, 接着通过0.5的概率分别运行两个在线算法得到一个组合随机算法, 最后证明了这个组合随机算法的期望竞争比不超过1.5。     

一类一维在线单位聚类问题的随机近似算法

在给定的度量空间中, 单位聚类问题就是寻找最少的单位球来覆盖给定的所有点。这是一个众所周知的组合优化问题, 其在线版本为: 给定一个度量空间, 其中的n个点会一个接一个的到达任何可能的位置, 在点到达的时候必须给该点分配一个单位聚类, 而此时未来点的相关信息都是未知的, 问题的目标是最后使用的单位聚类数目最少。本文考虑的是带如下假设的一类一维在线单位聚类问题: 在相应离线问题的最优解中任意两个相邻聚类之间的距离都大于0.5。本文首先给出了两个在线算法和一些引理, 接着通过0.5的概率分别运行两个在线算法得到一个组合随机算法, 最后证明了这个组合随机算法的期望竞争比不超过1.5。     

一簇非线性等式约束优化问题的过滤线搜索修正正割方法

本文提供了一簇新的过滤线搜索修正正割方法求解非线性等式约束优化问题.新算法簇的特点是:用修正正割算法簇中的一个算法获得搜索方向,回代线搜索技术得到步长,过滤准则用来决定是否接受步长,引入二阶校正技术减少不可行性并克服Maratos效应.在合理的假设条件下,分析了算法的总体收敛性.并证明了,通过附加二阶校正步,算法簇克服了Maratos效应,并二步Q-超线性收敛到满足二阶充分最优条件的局部解.数值结果表明了所提供的算法具有有效性.